Chapter 4: User Need Analysis through User Research Studies
5.1 Introduction
Based on the findings and insights from user research studies, a lightweight working prototype was developed as a proof-of-concept to demonstrate how future learning sys- tems for practical electronics laboratory sessions can be conceptualized and designed based on Mark Weser’s vision of ubiquitous computing (Weiser, 1991). To achieve this, we adopted mobile augmented reality (AR) and utilized the concept of embedding com- monly used objects in the laboratory with computational capabilities – based on the con- cept of “The MediaCup” (Gellersen et al., 1999). Such physical objects, with embedded computational and sensing capabilities, are referred to as Smart Objects (SO). AR was adopted owing to its potential to enable real-time interaction between the user, real ob- jects and virtual objects (i.e., digital data).
Secondly, during user research studies it was observed that students mostly carry digital tablets or smartphones, laboratory manual and a journal to maintain records of the experiment during practical laboratory sessions. Therefore, mobile AR provides a cost- effective and easy to deploy way to instruct and assist students in laboratory employing already existing mediums of smartphones and digital tablets. By utilizing the concept of SO, natural affordance of physical objects can be utilized which does not create an addi- tional learning curve on an already burdened learner in the complex environment of a practical laboratory. The SO also affords tactual interaction by retaining the physicality that helps promote better hands-on learning experience.
By coupling AR and SO, and utilizing a few features and functionalities of the Internet of Things (IoT) and Artificial Intelligence (AI), a Smart Learning System (SLS) was conceptualized and developed that could assist students effectively in practical elec- tronics laboratory sessions and facilitates teaching. Here, we would emphasize that our
AI design is for a set of static, deterministic conditions within well-bounded situations and utilizes if-else conditional expressions for making decisions. The topic of embedded AI and its design will be discussed later. Figure 5.1 (a) depicts the synergies between different technologies adopted and integrated into the prototypes conceptualized and de- veloped as a part of this thesis.
(a) (b)
Figure 5.1 Technological synergies and dominance of our proposed system. (a) Smart learning systems synergistically combine the functionalities and features of augmented reality, smart objects, internet of things
and artificial intelligence to assist students in their tasks during practical electronics laboratory session. (b) Dominant technological spaces utilized in the smart learning system. The prototype predominantly utilizes
augmented reality, followed by smart objects, artificial intelligence, and the internet of things.
The working SLS prototype mainly consists of AR and SO. Figure 5.2 shows a basic block diagram of its main components.
Figure 5.2 Smart Learning System components block diagram
During the design process of the prototype, laboratory course instructors were also interviewed to gain understanding regarding their needs and perspective on the use of technology in practical laboratory sessions. The insights from these interviews paved the way for the conceptualization of a system that also augments human tutoring and teach- ing. This led towards adopting the concept of IOT and its potential to implement AI for a large scale. Overall, the goal of the conceptualized system is to have a combined effect on improving student’s activity in the laboratory, their cognitive functioning in terms of
such complex practical laboratory sessions, education technology should be able to bal- ance both didactic and exploratory component of learning – thereby leading to a holistic experiential learning and teaching.
The proposed prototype (see Figure 5.6) is based on several layers of technological concepts that have explicitly been adopted from a user-centred design perspective to aid usability and utility during learning and teaching. Figure 5.1 (b) describes the technolog- ical spaces of SLS prototype concept which predominantly utilizes AR followed by the use of SO. Here a commonly used breadboard was chosen to be converted into a SO by embedding computational capabilities to it. This SO is referred to as an ‘intelligent bread- board’ – which was developed as a proof-of-concept to show how can existing artifacts from laboratories be adopted and made ‘smart’ to assist users.
Next, we considered the use of AI that can assist students in a manner similar to that of a human tutor – by encapsulating instructor’s tacit knowledge and instruction patterns.
We have considered AI that looks at learning from a contextual viewpoint, i.e., it is cul- tural and from a background of students and is only a layer in the whole scheme of things.
Here, we contend that the design of AI embedded into the system should be such that it can pinpoint:
• Where is a student going wrong/ committing errors?
• Why is he/ she going wrong?
• Which concept do they need to learn to understand such case?
• How to prompt instructions through which students can derive learning on their own, self-reflect upon their actions and gain the ability to understand where they are going wrong and why they are going wrong?
Finally, we would at this point also make it clear that our final design solution to the identified need is an AI-enabled product that augments users’ ability to work in the complex learning environment. We are not utilizing any AI algorithm but instead pro- posing a method utilizing UCD approach to design AI-enabled tool.
Lastly, we have adopted an IOT approach, which allows us to conceptualize how we can upscale this system so that it can accommodate the needs of the instructors as well as the students by enabling real-time data sharing and visualization. Therefore, by com- bining all the above layers of technology, our prototype provides an agile method for improving the learning experience in complex educational environments where sluggish
process hampers learning. In a large-scale scenario, this system will provide quality of learning content to students. An elaborate discussion on such a scenario is carried out later on in this chapter. Privacy issues of such system fall in future work considerations.